Welcome! While we're waiting:

• Please download the workshop files from: https://github.com/dlab-geo/r-leaflet-workshop

• Unzip the Zipfile: https://github.com/dlab-geo/r-leaflet-workshop/archive/master.zip

• Open RStudio

• Open a new R script file

Basic Maps

• Marker Maps
• Adding Popups

Data Maps

• Symbology for mapping point data
• Choropleth maps
• Adding Legends

Doing More

• Customizing the UI
• Sharing your maps

This workshop/tutorial will walk you through the basics of using the Leaflet mapping package in R. You can follow along in any of the three formats:

• Tutorial Page (leaflet-webmaps-in-R-sp2018.html)
• Raw code (leaflet-webmaps-in-R-sp2018.R)
• Slides (leaflet-webmaps-in-R-sp2018-slides.html)

Make sure you can copy and paste from one of the above into the script.

To begin, lets set up our packages and environment.

Load the packages we will use today

library(leaflet)
library(RColorBrewer)
library(sp)
library(rgdal)
library(htmlwidgets)
library(magrittr) # or dplyr

Install any packages that you do not have on your computer

# install.packages("leaflet")
# install.packages("RColorBrewer")
# install.packages("sp")
# install.packages("rgdal")
# install.packages("htmlwidgets")
# install.packages("magrittr") # or dplyr

to the folder in which you unzipped the workshop files

Leaflet is a lightweight, yet powerful javascript library for creating interactive web maps.

Leaflet maps are a combination of HTML and Javascript code that is meant to be rendered in a web browser.

We can use the R leaflet package to create Leaflet maps in R

map1 <- leaflet()       # Initialize the map object
map1 <- addTiles(map1)  # Add basemap tiles
map1                    # Display the map

map1 <- leaflet()       # Initialize the map object
map1 <- addTiles(map1)  # Add basemap tiles - default is OpenStreetMap
map1                    # Display the map

We specify the center and zoom level for the map

map1 <- leaflet() %>%
        addTiles() %>%  
        setView(lat=37.870044, lng=-122.258169, zoom = 15)
map1  

map1  # setView(lat=37.870044, lng=-122.258169, zoom = 15)

Requires dplyr or magrittr package to be loaded

map2 <- leaflet() %>%
        addTiles() %>%  
        setView(lat=37.870044, lng=-122.258169, zoom = 15)
map2   

map2

Regular

map1 <- leaflet()        
map1 <- addTiles(map1)   
map1 <- setView(map1, lat=37.870044, lng=-122.258169, zoom = 15)
map1                  

Piping

map2 <- leaflet() %>%
        addTiles() %>%  
        setView(lat=37.870044, lng=-122.258169, zoom = 15)
map2   

WARNING: Don't name your map object map

Rerun the piping code changing the zoom level

• What zoom level reveals the campus building that the map is centered on?
• What zoom level shows all of berkeley?
• What is the max/min zoom level that returns a basemap?

By Default, Leaflet uses the OpenStreetMap basemap, which is added with the addTiles() function

leaflet() %>% addTiles() %>% 
           setView(lat=37.870, lng=-122.258, zoom = 15)

Use addProviderTiles with the name of the basemap to add a different basemap.

Below, we are using the ESRI World Street Map basemap.

map2 <- leaflet() %>%
        addProviderTiles("Esri.WorldStreetMap") %>% 
        setView(lat=37.870044, lng=-122.258169, zoom = 12)

map2   #Using ESRI WorldStreetMap basemap

Try adding a different basemap.

For example, "CartoDB.Positron"

And open the web page of available basemaps

http://leaflet-extras.github.io/leaflet-providers/preview/

For more info, read the documentation

?addProviderTiles

leaflet() %>% addProviderTiles("CartoDB.Positron") %>% 
    setView(lat=37.870044, lng=-122.258169, zoom = 12)

You can add an online, georectified scanned map to leaflet.

There are many of these online at the New York Public Library or MapWarper, and other websites.

Let's add this map of Berkeley in 1880

Here we are combining addTiles and addProviderTiles

mapurl <- "https://mapwarper.net/maps/tile/25477/{z}/{x}/{y}.png"

map2 <- leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addTiles(mapurl) %>%  # custom map image
  setView(lat=37.870044, lng=-122.258169, zoom = 13)

map2  # Map of Berkeley, 1880 overlaid on the CartoDB basemap

Use addMarkers to add one or more data points to the map.

The map will automatically center on the data and determine an appropriate zoom level - if you don't use setView (commented out below).

You can override this by using setView.

map3 <- leaflet() %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  #setView(lat=37.870044, lng=-122.258169, zoom = 17) %>%
  addMarkers(lat=37.870044, lng=-122.258169, popup="Go Bears!")

map3  # Display the map  - Click on the marker

San Francisco Open Data Portal

SF Property Tax Rolls

This data set includes the Office of the Assessor-Recorder’s secured property tax roll spanning from 2015.

We are using this as a proxy for home values.

We are working with a simplified sample of the full data set.

Set your working directory first to the folder where you downloaded the workshop files!

sfhomes <- read.csv('data/sfhomes15.csv', stringsAsFactors = FALSE)  
str(sfhomes)

## 'data.frame':    680 obs. of  11 variables:
##  $ SalesDate   : chr  "2015-08-21" "2015-08-13" "2015-12-29" "2015-07-06" ...
##  $ Address     : chr  "0000 2760 19TH                AV0015" "0000 0560AMISSOURI            ST0000" "0000 0718 LONG BRIDGE         ST1202" "0000 0899 VALENCIA            ST0202" ...
##  $ YrBuilt     : int  1979 2003 2016 2015 1961 1900 2015 NA 1947 1907 ...
##  $ NumBeds     : int  2 2 2 3 3 2 2 0 0 3 ...
##  $ NumBaths    : int  2 2 2 3 3 2 2 0 0 3 ...
##  $ NumUnits    : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ AreaSqFt    : int  1595 1191 1346 1266 1840 1256 1520 536 950 1837 ...
##  $ Neighborhood: chr  "Twin Peaks" "Potrero Hill" "Mission Bay" "Mission" ...
##  $ Value       : int  865000 1402560 2260993 1700000 2309692 2700564 1925000 583768 944180 1750001 ...
##  $ lat         : num  37.7 37.8 37.8 37.8 37.8 ...
##  $ lon         : num  -122 -122 -122 -122 -122 ...

head(sfhomes)

##    SalesDate                              Address YrBuilt NumBeds NumBaths
## 1 2015-08-21 0000 2760 19TH                AV0015    1979       2        2
## 2 2015-08-13 0000 0560AMISSOURI            ST0000    2003       2        2
## 3 2015-12-29 0000 0718 LONG BRIDGE         ST1202    2016       2        2
## 4 2015-07-06 0000 0899 VALENCIA            ST0202    2015       3        3
## 5 2015-06-12 0000 1333 JONES               ST0808    1961       3        3
## 6 2015-04-14 0000 1904 BAKER               ST0000    1900       2        2
##   NumUnits AreaSqFt    Neighborhood   Value      lat       lon
## 1        1     1595      Twin Peaks  865000 37.73601 -122.4741
## 2        1     1191    Potrero Hill 1402560 37.75920 -122.3965
## 3        1     1346     Mission Bay 2260993 37.77181 -122.3942
## 4        1     1266         Mission 1700000 37.75876 -122.4210
## 5        1     1840        Nob Hill 2309692 37.79362 -122.4149
## 6        1     1256 Pacific Heights 2700564 37.78881 -122.4437

map4 <- leaflet() %>%
  addTiles() %>%   
  addMarkers(lat=sfhomes$lat, lng=sfhomes$lon, 
            popup= paste("Address:", sfhomes$Address,
                         "<br>", # add line break
                         "Property Value: ", sfhomes$Value))

map4    # How did we map the data frame?

We can save the popup code and re-use it instead of typing it over and over again.

popup_content <- paste("<b>Address:</b>", sfhomes$Address,"<br>", 
                       "<b>Property Value</b>: ", sfhomes$Value, "<br>",
                       "<b>Neighborhood:</b> ", sfhomes$Neighborhood, "<br>",
                       "<b>Num Bedrooms: </b>", sfhomes$NumBeds, "<br>",
                       "<b>Num Bathrooms:</b>", sfhomes$NumBaths
                       )



map4 <- leaflet() %>%
          addTiles() %>%   
          addMarkers(lat=sfhomes$lat, lng=sfhomes$lon, 
                     popup= popup_content)

leaflet() %>%  addTiles() %>%   
      addMarkers(lat=sfhomes$lat, lng=sfhomes$lon, popup= popup_content)

Instead of this:

leaflet() %>%  
  addTiles() %>%   
  addMarkers(lat=sfhomes$lat, lng=sfhomes$lon, popup= popup_content)

We can use this syntax:

leaflet(sfhomes) %>%
  addTiles() %>%   
  addMarkers(~lon, ~lat, popup = popup_content)

By passing in the name of the data object (sfhomes) to leaflet we can reference column names directly.

Below we are using the tilde (~) operator to map the data to the addMarkers function.

When the addMarkers function arguments lng= and lat= are not named they must be in the expected order (lng, lat).

leaflet(sfhomes) %>%
  addTiles() %>%   
  addMarkers(~lon, ~lat, popup = popup_content)

The map is too crowded with Markers.

Read the addMarker documentation for options to address this.

addMarkers(map, lng = NULL, lat = NULL, layerId = NULL, 
           group = NULL, icon = NULL, popup = NULL, 
           options = markerOptions(), 
           clusterOptions = NULL, clusterId = NULL, 
           data = getMapData(map))

map4 <- leaflet(sfhomes) %>%
  addTiles() %>%   
  addMarkers(~lon, ~lat, popup= popup_content,
            clusterOptions = 1)

map4  # Explore the Map - hover over a cluster marker, zoom in.

addCircleMarker

map4 <- leaflet(sfhomes) %>%
  addTiles() %>%   
  addCircleMarkers(~lon, ~lat, popup = popup_content)

addCircleMarker

map4 

addCircleMarkers(map, lng = NULL, lat = NULL, radius = 10, 
    layerId = NULL, group = NULL, stroke = TRUE, color = "#03F", 
    weight = 5, opacity = 0.5, 
    fill = TRUE, fillColor = color, ....)

Change color, radius and stroke weight of circle markers

map4 <- leaflet(sfhomes) %>%
  addTiles() %>%   
  addCircleMarkers(~lon, ~lat, popup = popup_content,
             color="white", radius=6, weight=2,   # stroke
             fillColor="red",fillOpacity = 0.75   # fill
             )

• Use colors() to see a list of all R named colors.
• You can expand on these with hexidecimal color values.

map4 

Art + Science

Finding the right symbology - size, color, shape, etc

and mapping it to your data

Often requires a classification scheme

We can symbolize the size of points by data values by making the radius of the circle a function of a data value.

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= NA, color="Red", weight=1, fillOpacity = 0,
             radius= ~NumBeds+2
             )

map4  # Size is a function of what variable?

Circles and CircleMarkers look quite similar.

Circle radii are specified in meters while CircleMarkers are specified in pixels.

map4b <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircles(~lon, ~lat, popup=popup_content,
             fillColor= NA, color="Red", 
             weight=1, fillOpacity = 0,
             radius= ~NumBeds*10
             )

map4b  # Compare map4 and map4b at different zoom levels

The RColorBrewer package is widely used to create color palettes for maps.

• A color palette is a set of colors

There are 3 different types of color palettes

1. Qualitative Color Palettes
2. Sequential Color Palettes
3. Diverging Color Palettes

Contrasting colors for categorical data

display.brewer.all(type="qual") 

display.brewer.pal(7, "Set3" )  # Try a different number of colors

For highlighting trends in numerical data

display.brewer.all(type="seq")

For highlighting the outliers

display.brewer.all(type="div")

Let's map sfhomes by the values in the Neighborhood column.

First, check out the RColorBrewer qualitative color palettes

display.brewer.all(type="qual")

leaflet::colorfactor takes as input a color palette and a domain that contains the full range of possible values to be mapped.

colorFactor(palette, domain, levels = NULL, ordered = FALSE,
  na.color = "#808080", alpha = FALSE, reverse = FALSE)

colorfactor returns a function specific to that domain that can be used to output a set of color values.

# Create a qualitative color palette
myColors <- colorFactor("Paired", sfhomes$Neighborhood) 

myColors <- colorFactor("Paired", sfhomes$Neighborhood) 
the_color_values <- myColors(sfhomes$Neighborhood)
length(the_color_values)

## [1] 680

length(the_color_values) == length(sfhomes$Neighborhood)

## [1] TRUE

unique(the_color_values)

##  [1] "#B15928" "#C4AAD2" "#E83F26" "#F57166" "#FBAE63" "#F6905B" "#3EA433"
##  [8] "#BFA19E" "#EDD279" "#4F8EC1" "#9BD277" "#FF9939" "#A6CEE3" "#7E54A6"
## [15] "#79A6A3" "#C3A176"

Using a color palette

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat,  
             popup= popup_content,
             fillColor= ~myColors(Neighborhood),
             radius=6, color=NA, weight=2, fillOpacity = 1
             )

map4  # what neighborhood has the most 2015 transactions?

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= ~myColors(Neighborhood),
             radius=6, color=NA, weight=2,fillOpacity = 1
             ) %>%
      addLegend(title = "Neighborhood", pal =  myColors,
                values = ~Neighborhood, opacity = 1, 
                position="bottomleft")

map4 

Let's map the homes by value.

First, check out the sequential color palettes

display.brewer.all(type="seq")

For simple linear scaling of colors to values use colorNumeric.

• This is used to create a proportional color map

colorNumeric(palette, domain, na.color = "#808080", alpha = FALSE,
  reverse = FALSE)

Create the numeric color mapping function

numColors <- colorNumeric("Reds", sfhomes$Value)

Apply the numColors color mapping function to create a proportional color map

map4 <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= ~numColors(Value),
             radius=6, color="grey", weight=1, fillOpacity = 1
             ) %>%
      addLegend(title = "Property Values", pal =  numColors,
                values = ~Value, opacity = 1, 
                position="bottomleft")

map4  # proportional color map

You can use colorQuantile to create a color palette based on quantile binning of the data. This is used to create a graduated color map.

colorQuantile(palette, domain, n = 4,
  probs = seq(0, 1, length.out = n + 1),
  na.color = "#808080", alpha = FALSE, reverse = FALSE)

The default is 4 bins, but you can set n manually (3 to 7)

Note: The colorBin function can be used to create color palettes based on different classification methods for binning the data, eg equal interval, natural breaks etc.

# Use colorQuantile to create a color function for the data
quantColors <- colorQuantile("Reds", sfhomes$Value, n=5)

# Use the color function in the leaflet map
map4b <- leaflet(sfhomes) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= ~quantColors(Value),
             radius=6, color="grey", weight=1,fillOpacity = 1
             ) 

map4b  # Graduated color map

map4b %>%  addLegend(title = "Value", pal =  quantColors,
                values = ~Value, opacity = 1, 
                position="bottomleft")

map5 <-map4b %>%   addLegend(pal = quantColors, values = ~Value,
                     title = "Property Value, 2015",
                     position="bottomleft",
                     opacity=1,
                     labFormat = function(type, cuts, p) {
                      n = length(cuts)
                      cuts = paste0("$", format(cuts[-n], big.mark=","), 
                              " - ", "$",format(cuts[-1], big.mark=","))
                      }
                   )

map5  # Graduated Color Map

What's happening here?

sfhomes_low2high <- sfhomes[order(sfhomes$Value, decreasing = FALSE),]

map5 <- leaflet(sfhomes_low2high) %>%
  addProviderTiles("CartoDB.Positron") %>%
  addCircleMarkers(~lon, ~lat, popup=popup_content,
             fillColor= ~quantColors(Value),
             radius=6, color="grey", weight=1,fillOpacity = 1
             ) 

map5  # points reordered from low to high value

Basic Maps

• addMarkers - Simple Marker Maps
• addCircleMarkers - Circle Marker Maps

Data Maps

• addCircles Proportional symbol maps
• colorFactor - Category Maps
• colorNumeric - Proportional color maps
• colorQuantile - Graduated color maps

We have been working with geographic data in data frames

The coordinates are longitude and latitude

Next up, more complex spatial objects

We can use the sp and rgdal packages to import, manipulate and map more complex spatial objects.

sp - R classes and methods for spatial data

rgdal - Functions for importing and transforming spatial data

Let's use these to import data in ESRI Shapefiles

Read in the data

sf_md_hhi <- readOGR(dsn="data",layer="sf_medhhincome_acs5y_16")

## OGR data source with driver: ESRI Shapefile 
## Source: "data", layer: "sf_medhhincome_acs5y_16"
## with 196 features
## It has 5 fields

summary(sf_md_hhi)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##          min        max
## x -123.01392 -122.32756
## y   37.69274   37.86334
## Is projected: FALSE 
## proj4string :
## [+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0]
## Data attributes:
##          GEOID    
##  06075010100:  1  
##  06075010200:  1  
##  06075010300:  1  
##  06075010400:  1  
##  06075010500:  1  
##  06075010600:  1  
##  (Other)    :190  
##                                                  NAME           variable  
##  Census Tract 101, San Francisco County, California:  1   B19013_001:196  
##  Census Tract 102, San Francisco County, California:  1                   
##  Census Tract 103, San Francisco County, California:  1                   
##  Census Tract 104, San Francisco County, California:  1                   
##  Census Tract 105, San Francisco County, California:  1                   
##  Census Tract 106, San Francisco County, California:  1                   
##  (Other)                                           :190                   
##     estimate           moe       
##  Min.   : 11971   Min.   :  739  
##  1st Qu.: 65893   1st Qu.: 9744  
##  Median : 89668   Median :15343  
##  Mean   : 91151   Mean   :16211  
##  3rd Qu.:117922   3rd Qu.:20032  
##  Max.   :205865   Max.   :89454  
##  NA's   :2        NA's   :2

Leaflet understands sp objects!!!

Map sf_md_hhi with addPolygons

map6 <- leaflet() %>%
  addTiles() %>%
  addPolygons(data=sf_md_hhi)

map6 # using addPolygons to map sf_md_hhi

?addPolygons

addPolygons(map, lng = NULL, lat = NULL, layerId = NULL, group = NULL,
            stroke = TRUE, color = "#03F", weight = 5, opacity = 0.5,
            fill = TRUE, fillColor = color, fillOpacity = 0.2, 
            dashArray = NULL, smoothFactor = 1, noClip = FALSE, 
            popup = NULL, popupOptions = NULL, label = NULL, 
            labelOptions = NULL, options = pathOptions(),
            highlightOptions = NULL, data = getMapData(map))

map6 <- leaflet() %>%
  setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
  addProviderTiles("CartoDB.Positron") %>%
  
  # Customize the symbology of the polygons
  addPolygons(data=sf_md_hhi, color="grey", weight=1,
              fillColor="Orange", fillOpacity = 0.25)

map6  # color="grey", weight=1, fillColor="Orange", fillOpacity = 0.25

Color regions based on data values.

The data values are classified into bins.

• Quantile classification is the default.

Each bin gets a unique color from a color palette.

Median Household Income is in the estimate column

Recipe:

1. Create a color function based on the values of estimate
2. Map the polygons setting the color to values produced by the color function

• First, select the name of the color palette

#display.brewer.all(type="seq") 

Then create the color mapping function

##
quantColors <- colorQuantile("YlOrRd", sf_md_hhi$estimate, n=5)

A choropleth map is a graduated color map of polygon data.

map6 <- leaflet() %>%
  setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
  addProviderTiles("CartoDB.Positron") %>%
  # 
  addPolygons(data=sf_md_hhi, 
              color="white", 
              weight=1, 
              opacity=0.5,
              fillColor=~quantColors(estimate), 
              fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate))

map6  # choropleth map of median household income by census tract

map6 <- map6 %>% addLegend(pal = quantColors, 
                   values = sf_md_hhi$estimate,
                   title = "Median HH Income",
                   position="bottomleft",
                   opacity=1,
                   labFormat = function(type, cuts, p) {
                     n = length(cuts)
                     cuts = paste0("$", format(cuts[-n], big.mark=","), 
                             " - ", "$",format(cuts[-1], big.mark=","))
                   }
)

map6

You can add multiple data layers to a leaflet map.

Let's add the sfhomes to the map

cheap <- sfhomes[sfhomes$Value < 1000000,]

map7 <- leaflet() %>%
  setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
  addProviderTiles("CartoDB.Positron") %>%
  
  # Median household income polygons
  addPolygons(data=sf_md_hhi, color="white", weight=1, opacity=0.5,
              fillColor=~quantColors(estimate), fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate)) %>%
  
  # sfhomes points
  addCircleMarkers(data=cheap, popup=paste0("$",cheap$Value),
              color="black",weight=1, radius=6, 
              fillColor="white", fillOpacity = 0.75)

map7  # sfhomes and median household income

We can add a layer switcher control with addLayersControl().

This allows us to toggle on/off the display of a map layer.

First, we need to assign a group to each map layer

?addLayersControl

map8 <- leaflet() %>%
          setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
          addProviderTiles("CartoDB.Positron") %>%
          addPolygons(data=sf_md_hhi, color="white", weight=1, opacity=0.5,
              fillColor=~quantColors(estimate), fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate),
              group="Median HH Income"
          ) %>%
          addCircleMarkers(data=cheap, popup=paste0("$",cheap$Value),
              color="black",weight=1, radius=6, 
              fillColor="white", fillOpacity = 0.75,
              group="Property Values"
          ) %>%
          addLayersControl(
            overlayGroups = c("Property Values","Median HH Income"),
            options = layersControlOptions(collapsed = FALSE)
        )

map8

map8 <- leaflet() %>%
          setView(lng=-122.448889, lat=37.764645, zoom=12) %>%
          addProviderTiles("CartoDB.Positron", group="Simple Basemap") %>%
          addProviderTiles("Esri.WorldStreetMap", group="Streets Basemap")  %>%
          addTiles("", group="No Basemap") %>%  
          #
          addPolygons(data=sf_md_hhi, color="white", weight=1, opacity=0.5,
              fillColor=~quantColors(estimate), fillOpacity = 0.65,
              popup = paste0("$",sf_md_hhi$estimate),
              group="Median HH Income"
          ) %>%
          addCircleMarkers(data=cheap, popup=paste0("$",cheap$Value),
              color="black",weight=1, radius=6, 
              fillColor="white", fillOpacity = 0.75,
              group="Property Values"
          ) %>%
          addLayersControl(
            baseGroups = c("Simple Basemap", "Streets Basemap", "No Basemap"),
            overlayGroups = c("Property Values","Median HH Income"),
            options = layersControlOptions(collapsed = FALSE)
        )

map8

Interactive Maps in R - check

Web Maps can be shared if online.

Easy way to go is RPubs

You can share you map online by publishing it to RPubs.

• You need to have an RPubs account to make that work.

1. Enter the code for your map in the console

2. In the Viewer window, click on the Publish icon.

DEMO… http://rpubs.com/pfrontiera/368070

Another way to share your map is to save it to a file. You can then email it, host it on your own web server or host it on github, etc.

#library(htmlwidgets)
saveWidget(map7, file="testmap.html")

Open your file to by double-clicking on it in the Mac Finder or Windows Explorer.

Getting more Practice

Leaflet for R Tutorial

tmap

• great for exploratory data analysis of sp objects within R
• much easier than leaflet for creating Leaflet maps as well as publication ready static map images.

mapview

• This is a new package similar to tmap. I haven't checked it out but I keep seeing it so I think it is emerging.

Powerful You can customize it to create great interactive maps

Integrates with Shiny, an R web Framework

Shiny User Gallery

Demo w/Tutorial at

https://cengel.shinyapps.io/RioSlaveMarket/

To you!

and to Josh Pepper who did an earlier workshop on which these materials are loosely based.